- [[linear models in transformed space are non-linear models in original space]], [[linear classifiers]] # Idea Kernel SVMs are computationally efficient algorithms that fit non-linear boundaries using [[linear classifiers]]. They exploit the fact that fitting [[linear classifiers]] in a transformed space corresponds to fitting non-linear models in the original space (see [[linear models in transformed space are non-linear models in original space|here]]). Kernels help to perform feature transformations in a computationally efficient way. # Python code ```python from sklearn.svm import SVC svm = SVC(gamma=1) # default is kernel='rbf'; larger gamma, more complex boundaries ``` ![[Pasted image 248.png]] # References - https://campus.datacamp.com/courses/linear-classifiers-in-python/support-vector-machines?ex=4